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      "metadata": {
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        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "In this notebook, we are going to fine-tune `LayoutLMForSequenceClassification` on the [RVL-CDIP dataset](https://www.cs.cmu.edu/~aharley/rvl-cdip/), which is a document image classification task. Each scanned document in the dataset belongs to one of 16 classes, such as \"resume\" or \"invoice\" (so it's a multiclass classification problem). The entire dataset consists of no less than 400,000 (!) scanned documents. \r\n",
        "\r\n",
        "For demonstration purposes, we are going to fine-tune the model on a really small subset (one example per class), and verify whether the model is able to overfit them. Note that LayoutLM achieves state-of-the-art results on RVL-CDIP, with a classification accuracy of 94.42% on the test set.\r\n",
        "\r\n",
        "* Original LayoutLM paper: https://arxiv.org/abs/1912.13318\r\n",
        "* LayoutLM docs in the Transformers library: https://huggingface.co/transformers/model_doc/layoutlm.html\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eb9O-5FdoCF9"
      },
      "source": [
        "## Setting up environment\r\n",
        "\r\n",
        "First, we install the 🤗 transformers and datasets libraries, as well as the [Tesseract OCR engine](https://github.com/tesseract-ocr/tesseract) (built by Google). LayoutLM requires an external OCR engine of choice to turn a document into a list of words and bounding boxes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9ALDa_1rrQO",
        "outputId": "511a7c27-2621-485a-95e8-b0b81b0bbb6e"
      },
      "source": [
        "! pip install transformers datasets"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->datasets) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->datasets) (2018.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PvlsFGjrjv-O",
        "outputId": "db4099a0-5c58-43cb-c309-84e92bd94abf"
      },
      "source": [
        "! sudo apt install tesseract-ocr\r\n",
        "! pip install pytesseract"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "tesseract-ocr is already the newest version (4.00~git2288-10f4998a-2).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.\n",
            "Requirement already satisfied: pytesseract in /usr/local/lib/python3.6/dist-packages (0.3.7)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.6/dist-packages (from pytesseract) (7.0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vbRjhABjF0J"
      },
      "source": [
        "## Getting the data\r\n",
        "\r\n",
        "Next, we download a small subset of the RVL-CDIP dataset (which I prepared), containing 15 documents (one example per class). I omitted the \"handwritten\" class, because the OCR results were mediocre. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wqaqmc3bQ2jr"
      },
      "source": [
        "import requests, zipfile, io\r\n",
        "\r\n",
        "def download_data():\r\n",
        "    url = \"https://www.dropbox.com/s/kuw05qmc4uy474d/RVL_CDIP_one_example_per_class.zip?dl=1\"\r\n",
        "    r = requests.get(url)\r\n",
        "    z = zipfile.ZipFile(io.BytesIO(r.content))\r\n",
        "    z.extractall()\r\n",
        "\r\n",
        "download_data()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gnlSn_06oMcC"
      },
      "source": [
        "Let's look at a random training example (in this case, a resume):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7akT268yjFhr",
        "outputId": "6a04ed81-bbb5-47ac-c5f3-db1099099d3a"
      },
      "source": [
        "from PIL import Image, ImageDraw, ImageFont\r\n",
        "\r\n",
        "image = Image.open(\"/content/RVL_CDIP_one_example_per_class/resume/0000157402.tif\")\r\n",
        "image = image.convert(\"RGB\")\r\n",
        "image"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=762x1000 at 0x7FE62AB4E390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yZHoAwT6y2RH"
      },
      "source": [
        "We can use the Tesseract OCR engine to turn the image into a list of recognized words:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "dxXvWaSzwGlD",
        "outputId": "e1ebc845-c63d-41eb-92f4-eff07f485ef4"
      },
      "source": [
        "import pytesseract\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "ocr_df = pytesseract.image_to_data(image, output_type='data.frame')\r\n",
        "ocr_df = ocr_df.dropna().reset_index(drop=True)\r\n",
        "float_cols = ocr_df.select_dtypes('float').columns\r\n",
        "ocr_df[float_cols] = ocr_df[float_cols].round(0).astype(int)\r\n",
        "ocr_df = ocr_df.replace(r'^\\s*$', np.nan, regex=True)\r\n",
        "words = ' '.join([word for word in ocr_df.text if str(word) != 'nan'])\r\n",
        "words"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"ot uv STATEMENT OF JEAN D, GIBBONS My name 4s Jean Dickinson Gibbons. My current position is Professor of Statistics and Chairman of the Applied Statistics Program at the Graduate School of the University of Alabana, I am currently a Fellow of both the American Statistical Association and the International Statistical Institute and a menber of the Committee on National Statistics of the National, Acadeny of Scdences. I received the bachelor's and master's degrees in mathematics from Duke University and the Ph.D. degree in statistics from Virginia Polytechnic Institute and State University. My previous faculty appointments were at the University of Pennsylvania and the University of Cincinnati. I was a senior Fulbright-Hays scholar at the Indian Statistical Institute in 1973. Twas Associate Editor of The Anertcan Statistician for eight years, currently act as editortal collaborator on many statistical journals, includ~ Technometrics, and serve as a reviewer for grant proposals to the National Science Foundation, I am @ member of several professional societies and have served two terms on the Board of Directors of the American Statistical Ascocdation. My publications include four scholarly books on statistics and over 30 articles in refereed professional and learned journals in my field, T was named Outstanding Scholar in 1981 and Board of Visitors Research Professor in 1974 at the University of Alabama, My current curriculua vita ig attached to this statement. stsepotzs\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FBaXAoSzuFl-"
      },
      "source": [
        "We can also visualize the bounding boxes of the recognized words, as follows:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6KKtDucCuKOg",
        "outputId": "d2f2f648-f1c0-412b-92ce-81fe5d4b4bdb"
      },
      "source": [
        "coordinates = ocr_df[['left', 'top', 'width', 'height']]\r\n",
        "actual_boxes = []\r\n",
        "for idx, row in coordinates.iterrows():\r\n",
        "    x, y, w, h = tuple(row) # the row comes in (left, top, width, height) format\r\n",
        "    actual_box = [x, y, x+w, y+h] # we turn it into (left, top, left+width, top+height) to get the actual box \r\n",
        "    actual_boxes.append(actual_box)\r\n",
        "\r\n",
        "draw = ImageDraw.Draw(image, \"RGB\")\r\n",
        "for box in actual_boxes:\r\n",
        "  draw.rectangle(box, outline='red')\r\n",
        "\r\n",
        "image"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=762x1000 at 0x7FE62AB4E390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VN-iboovriQL"
      },
      "source": [
        "## Preprocessing the data using 🤗 datasets\r\n",
        "\r\n",
        "First, we convert the dataset into a Pandas dataframe, having 2 columns: `image_path` and `label`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ey4xYRCCrXpo",
        "outputId": "3d1d66f6-4770-465e-a917-56bfa1d4d719"
      },
      "source": [
        "import pandas as pd\r\n",
        "import os\r\n",
        "\r\n",
        "dataset_path = \"/content/RVL_CDIP_one_example_per_class\"\r\n",
        "labels = [label for label in os.listdir(dataset_path)]\r\n",
        "idx2label = {v: k for v, k in enumerate(labels)}\r\n",
        "label2idx = {k: v for v, k in enumerate(labels)}\r\n",
        "label2idx"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'advertisement': 4,\n",
              " 'budget': 6,\n",
              " 'email': 13,\n",
              " 'file_folder': 10,\n",
              " 'form': 9,\n",
              " 'invoice': 3,\n",
              " 'letter': 11,\n",
              " 'memo': 8,\n",
              " 'news_article': 12,\n",
              " 'presentation': 1,\n",
              " 'questionnaire': 5,\n",
              " 'resume': 7,\n",
              " 'scientific_publication': 14,\n",
              " 'scientific_report': 2,\n",
              " 'specification': 0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "BdXrYSBq-Vjg",
        "outputId": "e2f8c7b8-6abc-4583-a708-9243a6937658"
      },
      "source": [
        "images = []\r\n",
        "labels = []\r\n",
        "\r\n",
        "for label_folder, _, file_names in os.walk(dataset_path):\r\n",
        "  if label_folder != dataset_path:\r\n",
        "    label = label_folder[40:]\r\n",
        "    for _, _, image_names in os.walk(label_folder):\r\n",
        "      relative_image_names = []\r\n",
        "      for image in image_names:\r\n",
        "        relative_image_names.append(dataset_path + \"/\" + label + \"/\" + image)\r\n",
        "      images.extend(relative_image_names)\r\n",
        "      labels.extend([label] * len (relative_image_names)) \r\n",
        "\r\n",
        "data = pd.DataFrame.from_dict({'image_path': images, 'label': labels})\r\n",
        "data.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_path</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/RVL_CDIP_one_example_per_class/specif...</td>\n",
              "      <td>specification</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/RVL_CDIP_one_example_per_class/presen...</td>\n",
              "      <td>presentation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/RVL_CDIP_one_example_per_class/scient...</td>\n",
              "      <td>scientific_report</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/RVL_CDIP_one_example_per_class/invoic...</td>\n",
              "      <td>invoice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/RVL_CDIP_one_example_per_class/advert...</td>\n",
              "      <td>advertisement</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                          image_path              label\n",
              "0  /content/RVL_CDIP_one_example_per_class/specif...      specification\n",
              "1  /content/RVL_CDIP_one_example_per_class/presen...       presentation\n",
              "2  /content/RVL_CDIP_one_example_per_class/scient...  scientific_report\n",
              "3  /content/RVL_CDIP_one_example_per_class/invoic...            invoice\n",
              "4  /content/RVL_CDIP_one_example_per_class/advert...      advertisement"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukXEAqpsJmiP",
        "outputId": "46e924d4-f4d8-4887-db58-61b05cb0f144"
      },
      "source": [
        "len(data)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SkMB9X8eh1K5"
      },
      "source": [
        "Now, let's apply OCR to get the words and bounding boxes of every image. To do this efficiently, we turn our Pandas dataframe into a HuggingFace `Dataset` object, and use the `.map()` functionality to get the words and normalized bounding boxes of every image. Note that this can take a while to run (Tesseract seems a bit slow)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "1193865036c44bd1a12686b83db13930",
            "e2557516931a4d8eaa045e224895547d",
            "7d2452cfb7874b75ad1737c2d34a0ecc",
            "fa412cbc839a4ff093a08fdabadee81b",
            "a40eb73ab716491d85487c2243601d78",
            "3d917f1d78ee4abb9ec700a5ddf08cee",
            "dfaeddd6844c48258a1b9984537871f2",
            "958947c076144318883744eaf719064e"
          ]
        },
        "id": "zMRgG40hh34E",
        "outputId": "dc242ee2-46be-4ead-b91e-9451a6d3ce65"
      },
      "source": [
        "from datasets import Dataset\r\n",
        "\r\n",
        "def normalize_box(box, width, height):\r\n",
        "     return [\r\n",
        "         int(1000 * (box[0] / width)),\r\n",
        "         int(1000 * (box[1] / height)),\r\n",
        "         int(1000 * (box[2] / width)),\r\n",
        "         int(1000 * (box[3] / height)),\r\n",
        "     ]\r\n",
        "\r\n",
        "def apply_ocr(example):\r\n",
        "        # get the image\r\n",
        "        image = Image.open(example['image_path'])\r\n",
        "\r\n",
        "        width, height = image.size\r\n",
        "        \r\n",
        "        # apply ocr to the image \r\n",
        "        ocr_df = pytesseract.image_to_data(image, output_type='data.frame')\r\n",
        "        float_cols = ocr_df.select_dtypes('float').columns\r\n",
        "        ocr_df = ocr_df.dropna().reset_index(drop=True)\r\n",
        "        ocr_df[float_cols] = ocr_df[float_cols].round(0).astype(int)\r\n",
        "        ocr_df = ocr_df.replace(r'^\\s*$', np.nan, regex=True)\r\n",
        "        ocr_df = ocr_df.dropna().reset_index(drop=True)\r\n",
        "\r\n",
        "        # get the words and actual (unnormalized) bounding boxes\r\n",
        "        #words = [word for word in ocr_df.text if str(word) != 'nan'])\r\n",
        "        words = list(ocr_df.text)\r\n",
        "        words = [str(w) for w in words]\r\n",
        "        coordinates = ocr_df[['left', 'top', 'width', 'height']]\r\n",
        "        actual_boxes = []\r\n",
        "        for idx, row in coordinates.iterrows():\r\n",
        "            x, y, w, h = tuple(row) # the row comes in (left, top, width, height) format\r\n",
        "            actual_box = [x, y, x+w, y+h] # we turn it into (left, top, left+width, top+height) to get the actual box \r\n",
        "            actual_boxes.append(actual_box)\r\n",
        "        \r\n",
        "        # normalize the bounding boxes\r\n",
        "        boxes = []\r\n",
        "        for box in actual_boxes:\r\n",
        "            boxes.append(normalize_box(box, width, height))\r\n",
        "        \r\n",
        "        # add as extra columns \r\n",
        "        assert len(words) == len(boxes)\r\n",
        "        example['words'] = words\r\n",
        "        example['bbox'] = boxes\r\n",
        "        return example\r\n",
        "\r\n",
        "dataset = Dataset.from_pandas(data)\r\n",
        "updated_dataset = dataset.map(apply_ocr)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1193865036c44bd1a12686b83db13930",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=15.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GbjY0kRSIFx"
      },
      "source": [
        "Next, we can turn the word-level 'words' and 'bbox' columns into token-level `input_ids`, `attention_mask`, `bbox` and `token_type_ids` using `LayoutLMTokenizer`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mg9vgcnBKC6Y"
      },
      "source": [
        "from transformers import LayoutLMTokenizer\r\n",
        "import torch\r\n",
        "\r\n",
        "tokenizer = LayoutLMTokenizer.from_pretrained(\"microsoft/layoutlm-base-uncased\")\r\n",
        "\r\n",
        "def encode_example(example, max_seq_length=512, pad_token_box=[0, 0, 0, 0]):\r\n",
        "  words = example['words']\r\n",
        "  normalized_word_boxes = example['bbox']\r\n",
        "\r\n",
        "  assert len(words) == len(normalized_word_boxes)\r\n",
        "\r\n",
        "  token_boxes = []\r\n",
        "  for word, box in zip(words, normalized_word_boxes):\r\n",
        "      word_tokens = tokenizer.tokenize(word)\r\n",
        "      token_boxes.extend([box] * len(word_tokens))\r\n",
        "  \r\n",
        "  # Truncation of token_boxes\r\n",
        "  special_tokens_count = 2 \r\n",
        "  if len(token_boxes) > max_seq_length - special_tokens_count:\r\n",
        "      token_boxes = token_boxes[: (max_seq_length - special_tokens_count)]\r\n",
        "  \r\n",
        "  # add bounding boxes of cls + sep tokens\r\n",
        "  token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]\r\n",
        "  \r\n",
        "  encoding = tokenizer(' '.join(words), padding='max_length', truncation=True)\r\n",
        "  # Padding of token_boxes up the bounding boxes to the sequence length.\r\n",
        "  input_ids = tokenizer(' '.join(words), truncation=True)[\"input_ids\"]\r\n",
        "  padding_length = max_seq_length - len(input_ids)\r\n",
        "  token_boxes += [pad_token_box] * padding_length\r\n",
        "  encoding['bbox'] = token_boxes\r\n",
        "  encoding['label'] = label2idx[example['label']]\r\n",
        "\r\n",
        "  assert len(encoding['input_ids']) == max_seq_length\r\n",
        "  assert len(encoding['attention_mask']) == max_seq_length\r\n",
        "  assert len(encoding['token_type_ids']) == max_seq_length\r\n",
        "  assert len(encoding['bbox']) == max_seq_length\r\n",
        "\r\n",
        "  return encoding"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "c4f3e9efb19042a5bc05a050cb0e2296",
            "e3e7984fd5ca47c99f0a08ad76ec2145",
            "09360d35edbc43a89281046aebb6a48a",
            "0c7a73647ba84b2499f3d3871a03e509",
            "3ea13836dd714a7583c828837b3cea59",
            "93b1c963fad74bd49a1262cebb148d9e",
            "0f00d7414d024de88f5b755c9e7ab7bd",
            "35b6e2f08678416dba6d833957276bbe"
          ]
        },
        "id": "IeksmkWwfKjH",
        "outputId": "78f6cb62-8868-42cd-9f96-bd692e1421db"
      },
      "source": [
        "from datasets import Features, Sequence, ClassLabel, Value, Array2D\r\n",
        "\r\n",
        "# we need to define the features ourselves as the bbox of LayoutLM are an extra feature\r\n",
        "features = Features({\r\n",
        "    'input_ids': Sequence(feature=Value(dtype='int64')),\r\n",
        "    'bbox': Array2D(dtype=\"int64\", shape=(512, 4)),\r\n",
        "    'attention_mask': Sequence(Value(dtype='int64')),\r\n",
        "    'token_type_ids': Sequence(Value(dtype='int64')),\r\n",
        "    'label': ClassLabel(names=['refuted', 'entailed']),\r\n",
        "    'image_path': Value(dtype='string'),\r\n",
        "    'words': Sequence(feature=Value(dtype='string')),\r\n",
        "})\r\n",
        "\r\n",
        "encoded_dataset = updated_dataset.map(lambda example: encode_example(example), \r\n",
        "                                      features=features)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c4f3e9efb19042a5bc05a050cb0e2296",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=15.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UA8ZiWcr2Rwo"
      },
      "source": [
        "Finally, we set the format to PyTorch, as the LayoutLM implementation in the Transformers library is in PyTorch. We also specify which columns we are going to use."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZWg6ArO003By"
      },
      "source": [
        "encoded_dataset.set_format(type='torch', columns=['input_ids', 'bbox', 'attention_mask', 'token_type_ids', 'label'])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1Qdjf8o37IN",
        "outputId": "d1aab2bc-fe5a-4e0b-987d-0018e0234cb6"
      },
      "source": [
        "dataloader = torch.utils.data.DataLoader(encoded_dataset, batch_size=1, shuffle=True)\r\n",
        "batch = next(iter(dataloader))\r\n",
        "batch"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:851: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.tensor(x, **format_kwargs)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1]]), 'bbox': tensor([[[   0,    0,    0,    0],\n",
              "          [ 153,   56,  177,   66],\n",
              "          [ 153,   56,  177,   66],\n",
              "          ...,\n",
              "          [ 486,  675,  564,  686],\n",
              "          [ 486,  675,  564,  686],\n",
              "          [1000, 1000, 1000, 1000]]]), 'input_ids': tensor([[  101, 19817,  1010,  1054,  2232,  1010, 14135,  1006,  1007,  2139,\n",
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              "           2638,  4895,  1009,  1080, 14142,  1003, 28855,  1013,  3963,  1003,\n",
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              "           2389,  1021,  1012,  6486,  2575,  1043, 15068,  2546,  1062,  2401,\n",
              "           4697,  3401,  1997,  1996,  3449, 18796,  2094, 12884,  2015, 26822,\n",
              "           2615,  2008, 25423,  5054,  7629,  9215,  2012,  2102,  1997, 19413,\n",
              "           2063, 11190,  1027,  9044,  1018,  1045,  3449, 12926,  1999, 12884,\n",
              "           2015,  1001,  1019,  1998,  1020,  1010,  2122,  1055,  3630, 26432,\n",
              "           8490,   102]]), 'label': tensor([8]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "          0, 0, 0, 0, 0, 0, 0, 0]])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxmfktUy-o_E"
      },
      "source": [
        "Let's verify whether the input ids are created correctly by decoding them back to text:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "id": "3rJJ9WU7yTZC",
        "outputId": "e640f9a2-df28-436d-e0e0-6af56d1b564c"
      },
      "source": [
        "tokenizer.decode(batch['input_ids'][0].tolist())"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'[CLS] tr, rh, griffith ( ) de. re 2. jobaisen / oja 8 february 1963, chromatography of arona concentrates 38 oer continuing : investigations on the separation of tobacco acum con = sgakeace constituents ws have found that columa clioaatograply of evade aren see pence on ilies gol ie excellene tor their initial fractlostions tate ‘ hee becona the basis for an improved isolation schema for cossovod a experinental suey of 23 - 70 mash silica gel iu hexane is added to a 1 em coluen untid sre atticn sol bed depth 1s 39 eu. a 2. 0 ¢ gample af a erude hgh eeopecacuee seagjcekemerate 48 then applied eo the top of the coluan in hexane, eluring je rsefemplisted ftree with hexane, and then with solvents of gradwally increasing poreritys, rections of 100 mi are collegtad and tho weights of eluted arenes of the flaereceat soven : renoval at 20° / 10 axe mesules fron the chrouatastapiy 7 of the flue - eured sud burley aruna concentrates are tabulated belove burley fluo - cured ee reaction ¢ solvent we. eluted hts eluted t hexane + 713 g 2 hexaaa 2062 : 3 2 % ether / 98 % hexane 1026 im 4 2 % ethec / 98 % hexane 1061 2 hg 5 3 % ether / 95 % hexane wee 6 - + be ether / 95 % hexane “ e 7 \\\\ y 10 % echer / 90 % hexane se a 10 % ether / 90 % hexane ere 9 20 % ether / 80 % hexane : 10 20 % etixer / 60 % hexane un + °30 % ether / 70 % hexane 2 30 % ethor / 707 hexane 13 100 % - echer i 100 % etner 15 ethanol nee * toral 7. 896 g ouf ziaizece of the eluced fractions shov that uasentinlly att of eae cow = pound 4 i eluted in fractions # 5 and 6, these snomeiag [SEP]'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "_qGimR6HyZMy",
        "outputId": "00cf59bf-a164-4ab0-cd5f-3dc7947ae1fd"
      },
      "source": [
        "idx2label[batch['label'][0].item()]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'memo'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "28KGi6lqid0c"
      },
      "source": [
        "## Define the model\r\n",
        "\r\n",
        "Here we define the model, namely `LayoutLMForSequenceClassification`. We initialize it with the weights of the pre-trained base model (`LayoutLMModel`). The weights of the classification head are randomly initialized, and will be fine-tuned together with the weights of the base model on our tiny dataset. Once loaded, we move it to the GPU.\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlb_izEJjy3Z",
        "outputId": "f6981801-31c8-4d29-aab3-a6250c3216cb"
      },
      "source": [
        "from transformers import LayoutLMForSequenceClassification\r\n",
        "import torch\r\n",
        "\r\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\n",
        "\r\n",
        "model = LayoutLMForSequenceClassification.from_pretrained(\"microsoft/layoutlm-base-uncased\", num_labels=len(label2idx))\r\n",
        "model.to(device)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at microsoft/layoutlm-base-uncased were not used when initializing LayoutLMForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias']\n",
            "- This IS expected if you are initializing LayoutLMForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing LayoutLMForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of LayoutLMForSequenceClassification were not initialized from the model checkpoint at microsoft/layoutlm-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LayoutLMForSequenceClassification(\n",
              "  (layoutlm): LayoutLMModel(\n",
              "    (embeddings): LayoutLMEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (x_position_embeddings): Embedding(1024, 768)\n",
              "      (y_position_embeddings): Embedding(1024, 768)\n",
              "      (h_position_embeddings): Embedding(1024, 768)\n",
              "      (w_position_embeddings): Embedding(1024, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): LayoutLMEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): LayoutLMLayer(\n",
              "          (attention): LayoutLMAttention(\n",
              "            (self): LayoutLMSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): LayoutLMSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): LayoutLMIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): LayoutLMOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): LayoutLMPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=15, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IE9wo9Y1j3i8"
      },
      "source": [
        "## Train the model\r\n",
        "\r\n",
        "Here we train the model in familiar PyTorch fashion. We use the Adam optimizer with weight decay fix (normally you can also specify which variables should have weight decay and which not + a learning rate scheduler, see [here](https://github.com/microsoft/unilm/blob/5d16c846bec56b6e88ec7de4fc3ceb7c803571a4/layoutlm/examples/classification/run_classification.py#L94) for how the authors of LayoutLM did this), and train for 30 epochs. If the model is able to overfit it, then it means there are no issues and we can train it on the entire dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QZpQzFNj63T",
        "outputId": "616049ad-7f17-4659-d3d9-e195ac8c645f"
      },
      "source": [
        "from transformers import AdamW\r\n",
        "\r\n",
        "optimizer = AdamW(model.parameters(), lr=5e-5)\r\n",
        "\r\n",
        "global_step = 0\r\n",
        "num_train_epochs = 30\r\n",
        "t_total = len(dataloader) * num_train_epochs # total number of training steps \r\n",
        "\r\n",
        "#put the model in training mode\r\n",
        "model.train()\r\n",
        "for epoch in range(num_train_epochs):\r\n",
        "  print(\"Epoch:\", epoch)\r\n",
        "  running_loss = 0.0\r\n",
        "  correct = 0\r\n",
        "  for batch in dataloader:\r\n",
        "      input_ids = batch[\"input_ids\"].to(device)\r\n",
        "      bbox = batch[\"bbox\"].to(device)\r\n",
        "      attention_mask = batch[\"attention_mask\"].to(device)\r\n",
        "      token_type_ids = batch[\"token_type_ids\"].to(device)\r\n",
        "      labels = batch[\"label\"].to(device)\r\n",
        "\r\n",
        "      # forward pass\r\n",
        "      outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids,\r\n",
        "                      labels=labels)\r\n",
        "      loss = outputs.loss\r\n",
        "\r\n",
        "      running_loss += loss.item()\r\n",
        "      predictions = outputs.logits.argmax(-1)\r\n",
        "      correct += (predictions == labels).float().sum()\r\n",
        "\r\n",
        "      # backward pass to get the gradients \r\n",
        "      loss.backward()\r\n",
        "\r\n",
        "      # update\r\n",
        "      optimizer.step()\r\n",
        "      optimizer.zero_grad()\r\n",
        "      global_step += 1\r\n",
        "  \r\n",
        "  print(\"Loss:\", running_loss / batch[\"input_ids\"].shape[0])\r\n",
        "  accuracy = 100 * correct / len(data)\r\n",
        "  print(\"Training accuracy:\", accuracy.item())"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0\n",
            "Loss: 43.12694573402405\n",
            "Training accuracy: 0.0\n",
            "Epoch: 1\n",
            "Loss: 35.339784145355225\n",
            "Training accuracy: 33.333335876464844\n",
            "Epoch: 2\n",
            "Loss: 30.55066680908203\n",
            "Training accuracy: 86.66667175292969\n",
            "Epoch: 3\n",
            "Loss: 27.842902660369873\n",
            "Training accuracy: 86.66667175292969\n",
            "Epoch: 4\n",
            "Loss: 21.45477443933487\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 5\n",
            "Loss: 19.765377640724182\n",
            "Training accuracy: 93.33333587646484\n",
            "Epoch: 6\n",
            "Loss: 16.269045293331146\n",
            "Training accuracy: 93.33333587646484\n",
            "Epoch: 7\n",
            "Loss: 13.61208951473236\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 8\n",
            "Loss: 12.580875396728516\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 9\n",
            "Loss: 9.917366653680801\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 10\n",
            "Loss: 8.083266794681549\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 11\n",
            "Loss: 8.36630928516388\n",
            "Training accuracy: 93.33333587646484\n",
            "Epoch: 12\n",
            "Loss: 7.368062764406204\n",
            "Training accuracy: 93.33333587646484\n",
            "Epoch: 13\n",
            "Loss: 5.720428228378296\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 14\n",
            "Loss: 4.611804857850075\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 15\n",
            "Loss: 3.4929953813552856\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 16\n",
            "Loss: 2.9797599762678146\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 17\n",
            "Loss: 2.7349031269550323\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 18\n",
            "Loss: 2.3549284264445305\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 19\n",
            "Loss: 1.9186500012874603\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 20\n",
            "Loss: 1.960314616560936\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 21\n",
            "Loss: 1.8035170659422874\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 22\n",
            "Loss: 1.674522079527378\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 23\n",
            "Loss: 1.542198833078146\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 24\n",
            "Loss: 1.4831208437681198\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 25\n",
            "Loss: 1.3329095914959908\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 26\n",
            "Loss: 1.3143288791179657\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 27\n",
            "Loss: 1.1933068744838238\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 28\n",
            "Loss: 1.1401441916823387\n",
            "Training accuracy: 100.00000762939453\n",
            "Epoch: 29\n",
            "Loss: 1.0487095415592194\n",
            "Training accuracy: 100.00000762939453\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-IXZeC788R8"
      },
      "source": [
        "## Legacy\r\n",
        "\r\n",
        "The code below was used during the creation of this notebook, but is now not used anymore."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gt17gKiCqUmK"
      },
      "source": [
        "import torch\r\n",
        "\r\n",
        "def convert_example_to_features(image, words, boxes, actual_boxes, tokenizer, max_seq_length=512, cls_token_box=[0, 0, 0, 0],\r\n",
        "                                 sep_token_box=[1000, 1000, 1000, 1000],\r\n",
        "                                 pad_token_box=[0, 0, 0, 0]):\r\n",
        "      width, height = image.size\r\n",
        "\r\n",
        "      tokens = []\r\n",
        "      token_boxes = []\r\n",
        "      actual_bboxes = [] # we use an extra b because actual_boxes is already used\r\n",
        "      token_actual_boxes = []\r\n",
        "      for word, box, actual_bbox in zip(words, boxes, actual_boxes):\r\n",
        "          word_tokens = tokenizer.tokenize(word)\r\n",
        "          tokens.extend(word_tokens)\r\n",
        "          token_boxes.extend([box] * len(word_tokens))\r\n",
        "          actual_bboxes.extend([actual_bbox] * len(word_tokens))\r\n",
        "          token_actual_boxes.extend([actual_bbox] * len(word_tokens))\r\n",
        "\r\n",
        "      # Truncation: account for [CLS] and [SEP] with \"- 2\". \r\n",
        "      special_tokens_count = 2 \r\n",
        "      if len(tokens) > max_seq_length - special_tokens_count:\r\n",
        "          tokens = tokens[: (max_seq_length - special_tokens_count)]\r\n",
        "          token_boxes = token_boxes[: (max_seq_length - special_tokens_count)]\r\n",
        "          actual_bboxes = actual_bboxes[: (max_seq_length - special_tokens_count)]\r\n",
        "          token_actual_boxes = token_actual_boxes[: (max_seq_length - special_tokens_count)]\r\n",
        "\r\n",
        "      # add [SEP] token, with corresponding token boxes and actual boxes\r\n",
        "      tokens += [tokenizer.sep_token]\r\n",
        "      token_boxes += [sep_token_box]\r\n",
        "      actual_bboxes += [[0, 0, width, height]]\r\n",
        "      token_actual_boxes += [[0, 0, width, height]]\r\n",
        "      \r\n",
        "      segment_ids = [0] * len(tokens)\r\n",
        "\r\n",
        "      # next: [CLS] token\r\n",
        "      tokens = [tokenizer.cls_token] + tokens\r\n",
        "      token_boxes = [cls_token_box] + token_boxes\r\n",
        "      actual_bboxes = [[0, 0, width, height]] + actual_bboxes\r\n",
        "      token_actual_boxes = [[0, 0, width, height]] + token_actual_boxes\r\n",
        "      segment_ids = [1] + segment_ids\r\n",
        "\r\n",
        "      input_ids = tokenizer.convert_tokens_to_ids(tokens)\r\n",
        "\r\n",
        "      # The mask has 1 for real tokens and 0 for padding tokens. Only real\r\n",
        "      # tokens are attended to.\r\n",
        "      input_mask = [1] * len(input_ids)\r\n",
        "\r\n",
        "      # Zero-pad up to the sequence length.\r\n",
        "      padding_length = max_seq_length - len(input_ids)\r\n",
        "      input_ids += [tokenizer.pad_token_id] * padding_length\r\n",
        "      input_mask += [0] * padding_length\r\n",
        "      segment_ids += [tokenizer.pad_token_id] * padding_length\r\n",
        "      token_boxes += [pad_token_box] * padding_length\r\n",
        "      token_actual_boxes += [pad_token_box] * padding_length\r\n",
        "\r\n",
        "      assert len(input_ids) == max_seq_length\r\n",
        "      assert len(input_mask) == max_seq_length\r\n",
        "      assert len(segment_ids) == max_seq_length\r\n",
        "      assert len(token_boxes) == max_seq_length\r\n",
        "      assert len(token_actual_boxes) == max_seq_length\r\n",
        "      \r\n",
        "      encoding = {}\r\n",
        "      \r\n",
        "      encoding[\"input_ids\"] = torch.tensor(input_ids)\r\n",
        "      encoding[\"bbox\"] = torch.tensor(token_boxes)\r\n",
        "      encoding[\"attention_mask\"] = torch.tensor(input_mask)\r\n",
        "      encoding[\"token_type_ids\"] = torch.tensor(segment_ids)\r\n",
        "\r\n",
        "      return encoding"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eT-wTZLNiimV"
      },
      "source": [
        "from torch.utils.data import Dataset, DataLoader\r\n",
        "import pytesseract\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "class Dataset(Dataset):\r\n",
        "    \"\"\"RVL-CDIP dataset (small subset).\"\"\"\r\n",
        "\r\n",
        "    def __init__(self, data, tokenizer):\r\n",
        "        self.data = data\r\n",
        "        self.tokenizer = tokenizer\r\n",
        "\r\n",
        "    def __len__(self):\r\n",
        "        return len(self.data)\r\n",
        "\r\n",
        "    def __getitem__(self, idx):\r\n",
        "        # get the image\r\n",
        "        image = Image.open(data.iloc[idx].image_path)\r\n",
        "\r\n",
        "        width, height = image.size\r\n",
        "        \r\n",
        "        # apply ocr to the image \r\n",
        "        ocr_df = pytesseract.image_to_data(image, output_type='data.frame')\r\n",
        "        float_cols = ocr_df.select_dtypes('float').columns\r\n",
        "        ocr_df[float_cols] = ocr_df[float_cols].round(0).astype(int)\r\n",
        "        ocr_df = ocr_df.replace(r'^\\s*$', np.nan, regex=True)\r\n",
        "        ocr_df = ocr_df.dropna().reset_index(drop=True)\r\n",
        "\r\n",
        "        # get the words and actual (unnormalized) bounding boxes\r\n",
        "        words = list(ocr_df.text)\r\n",
        "        coordinates = ocr_df[['left', 'top', 'width', 'height']]\r\n",
        "        actual_boxes = []\r\n",
        "        for idx, row in coordinates.iterrows():\r\n",
        "            x, y, w, h = tuple(row) # the row comes in (left, top, width, height) format\r\n",
        "            actual_box = [x, y, x+w, y+h] # we turn it into (left, top, left+widght, top+height) to get the actual box \r\n",
        "            actual_boxes.append(actual_box)\r\n",
        "        \r\n",
        "        # normalize the bounding boxes\r\n",
        "        boxes = []\r\n",
        "        for box in actual_boxes:\r\n",
        "            boxes.append(normalize_box(box, width, height))\r\n",
        "\r\n",
        "        # convert to token-level features\r\n",
        "        encoding = convert_example_to_features(image, words, boxes, actual_boxes, self.tokenizer)\r\n",
        "\r\n",
        "        # add the label\r\n",
        "        print(idx)\r\n",
        "        label = data.iloc[idx].label\r\n",
        "        encoding[\"label\"] = torch.tensor(label2idx[label])\r\n",
        "        \r\n",
        "        return encoding"
      ],
      "execution_count": 20,
      "outputs": []
    }
  ]
}